The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.

Steering control is vital for hillside tractors, and four-wheel-steering technology significantly enhances stability and flexibility. However, existing methods often overlook the slope–stability relationship. In order to address this issue, considering the impact of tractor wheels on the steering characteristics, a nonlinear three-degree-of-freedom dynamic model based on the basic requirements within a 15° range of slope angles was derived. Subsequently, a four-wheel steering control strategy for hillside tractors was designed based on this model, incorporating a fuzzy PID control algorithm. The dynamics model was validated on the high-fidelity Carsim/Simulink co-simulation platform, and relevant experiments were conducted in Matlab/Simulink. Results showed that fuzzy PID control reduced the yaw rate’s average settling time from 0.36 s to 0.1 s and the response value from 0.28 rad/s to 0.038 rad/s. In addition, the lateral velocity and sideslip angle responses closely matched ideal values. Thus, the tractor with four-wheel steering under fuzzy PID control exhibited improved wheel angle flexibility and higher tracking accuracy. Finally, hardware-in-the-loop-experiments were conducted, confirming the algorithm’s effectiveness in ensuring stability and meeting the requirements of semi-physical simulation scenarios. This research provides a foundation for potential applications in tractor manufacturing to improve control performance on hilly terrains.

Polymer nanocomposites filled with highly thermally conductive nano/microparticles have shown promise for efficient thermal management in power electronics due to their excellent flexibility and thermal conductivity. However, it remains challenging to align particulate fillers into an optimal heat transfer path through a fast and large-scale green fabrication process, especially when adapting to various types of fillers and flexible matrices. Herein, we report a unique rolled sonication-enabled microstructure orientation (RSMO) method that enables superfast densification and orientation of various fillers in polymers through the synergistic thermo-mechanical effects of high-frequency vibration with roller pressing, achieving full processing within one second without external heating. Ultrasound-induced dense packing and oriented structures synergistically enhance both thermal conductivity (an increase of 171%) and tensile strength (19-fold enhancement) compared with simple roller pressing. RSMO has been demonstrated to be applicable to a wide range of fillers, including metals, inorganic nonmetals, and organic materials, as well as various film-forming techniques, such as electrospinning and screen printing. We successfully fabricated large-area thermally conductive films using RSMO and demonstrated their thermal management capabilities in power electronics and flexible thermoelectric generation, thereby confirming their significant potential in preparing high-performance thermal management materials for flexible electronic devices.

The positional deviation of the in-vehicle Automatic Emergency Call System (AECS) under collision conditions brings difficulties for Intelligent Connected Vehicles (ICVs) post rescue operations. Currently, there is a lack of analysis on system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in the AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on Signal-Based In-Vehicle Emergency Call Systems. We also propose environmental limits applicable to comprehensive tests, which can objectively evaluate reliability and provide data support for the AECS bench test through a satellite-signal-based semi-physical simulation, which is subjected to a bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibration and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4 gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature difference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of the satellite signal simulation, necessary environmental conditions for the test are obtained, including a temperature that is consistent with the maximum operating temperature of the vector generator and a vibration power spectral density (PSD) lower than 1.2 gRMS.